Latent inter-organ mechanism of idiopathic pulmonary fibrosis unveiled by a generative computational approach

Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive disease characterized by complex lung pathogenesis affecting approximately three million people worldwide. While the molecular and cellular details of the IPF mechanism is emerging, our current understanding is centered around the lung itself. On the other hand, many human diseases are the products of complex multi-organ interactions. Hence, we postulate that a dysfunctional crosstalk of the lung with other organs plays a causative role in the onset, progression and/or complications of IPF. In this study, we employed a generative computational approach to identify such inter-organ mechanism of IPF. This approach found unexpected molecular relatedness of IPF to neoplasm, diabetes, Alzheimer’s disease, obesity, atherosclerosis, and arteriosclerosis. Furthermore, as a potential mechanism underlying this relatedness, we uncovered a putative molecular crosstalk system across the lung and the liver. In this inter-organ system, a secreted protein, kininogen 1, from hepatocytes in the liver interacts with its receptor, bradykinin receptor B1 in the lung. This ligand–receptor interaction across the liver and the lung leads to the activation of calmodulin pathways in the lung, leading to the activation of interleukin 6 and phosphoenolpyruvate carboxykinase 1 pathway across these organs. Importantly, we retrospectively identified several pre-clinical and clinical evidence supporting this inter-organ mechanism of IPF. In conclusion, such feedforward and feedback loop system across the lung and the liver provides a unique opportunity for the development of the treatment and/or diagnosis of IPF. Furthermore, the result illustrates a generative computational framework for machine-mediated synthesis of mechanisms that facilitates and complements the traditional experimental approaches in biomedical sciences.

Idiopathic pulmonary fibrosis (IPF) is a chronic disease characterized by scarring in the interstitium of the lung, affecting 3-9 and 4 or less per 100,000 person-years in North America/Europe and South America/East-Asia, respectively 1,2 .Both the incidence and poor prognosis of IPF increase with age 3,4 .Specifically, the median age of the newly diagnosed is 62 years-old and their prognosis is poor-3-5 years of survival rate.
There are two Food and Drug Administration (FDA)-approved drugs for the treatment of IPF: nintedanib and pirfenidone 1,2 .Nintedanib is a tyrosine kinase inhibitor.Pirfenidone is an inhibitor of transforming growth factor (TGF)-beta production and downstream signaling, collagen synthesis and fibroblast proliferation.Hence, these drugs are regarded as pleiotropic anti-fibrosis drugs.Currently there are no IPF-specific therapeutics.Furthermore, the precise IPF diagnosis requires complex and multiple-types of tests as its overlapping pathologies with other interstitial lung fibrosis diseases 1,2 .These are in part due to the complexity of the IPF pathogenesis and to its ill-defined cellular and molecular mechanisms.
The molecular relatedness of IPF to non-respiratory/non-pulmonary diseases were identified by using the multimodal generative topic modeling method that we developed and previously reported 29 .The overall design is summarized in Fig. 1, and it works as follows: Two datasets are used for the multi-modal generative topic modeling: Datasets A and B. Dataset A consists of 6,954 human diseases excluding IPF, each of which is characterized by three disease omics modalities, AlteredExpression (Ae), Biomarker (Bm), and GeneticVariation (Gv), derived from DisGeNET v7.0 19,27 ."Ae" is the list of genes and proteins of which changes in expressions are associated with a corresponding disease(s)."Bm" is the list of biomarkers which are described for a corresponding disease(s)."Gv" is the list of genes of which mutations are reported for a corresponding disease(s).Dataset B consists of three types of IPF modality combination, each consisting of Bm/Gv (i.e., missing Ae), Gv/Ae (i.e., missing Bm), or Ae/Bm (i.e., missing Gv), also from DisGeNET v7.0 19,27 .The multi-modal generative topic modeling generates (i.e., predicts) the features of the missing modalities.The performance was evaluated by calculating the area under the receiver operating characteristic curve (AUC) values as previously described 29 and they were found to be above 0.8 for all three modalities (Supplementary Fig. S1).Next, from these computationally generated features, those derived from the modalities of IPF itself and those of obviously IPF-related diseases are removed.The diseases that are obviously related are those of which names contain "Pulmonary", "Lung", "Fibrosis", "Respir ** ", "Chest", "Pneumo**" (**could be any characters).The remaining features are now designated as "latent disease-omics features of IPF (also referred to as IPF-features)".Moreover, IPF and the diseases from which these IPF-features are derived in Dataset A establish "latent relatedness of IPF to other diseases".
Using this approach, we identified 83 latent IPF-features (Supplementary Table S1).The human-organexpression analysis using THE HUMAN PROTEIN ATLAS v 21.1 [23][24][25] (see also "Methods" section) found that their expression is most enriched in the liver (Fig. 2A, Supplementary Table S2).Additionally, we also detected the statistically significant (i.e., q-values < 0.05 ) enrichments in the immune system (bone marrow, lymphoid tissue, blood), the kidney, the thyroid gland, adipose tissue, the prostate, and the placenta.The cellular level analysis found the highest enrichment in the hepatocytes (Fig. 2B, Supplementary Table S2).In addition, we also detected the statistically significant (i.e., q-values < 0.05 ) enrichments in Kupffer cells and Hofbauer cells,

Inter-organ mechanism of IPF
A putative inter-organ mechanism was computationally generated as described in Fig. 4. The step-by-step description (Steps 1-7) and the results from each step are as follows: Step 1: To identify the ligands in the lung differentially expressed genes (DEgenes) (via CellChatDB, a human ligand-receptor combination database, as described in Fig. 4).
In IPF treatments, distinguishing IPF from the other non-IPF lung diseases is most critical for the better outcome [1][2][3][4][5][6] .Therefore, we analyzed DEgenes between IPF and non-IPF lung diseases subjects, rather than those between IPF and healthy subjects.
The DESeq2 analysis of the lung tissues obtained from 95 IPF and 204 non-IPF (unclassifiable interstitial pneumonia: UCIP, idiopathic nonspecific interstitial pneumonia: NSIP, idiopathic pleuroparenchymal fibroelastosis: PPFE, other idiopathic interstitial pneumonias: IIPs, hypersensitivity pneumonitis: HP, connective tissue diseases: CTD, and other interstitial lung disease) lung disease patients (see also "Methods" section) identified a total of 112 IPF-DEgenes (Supplementary Table S4).General overview of the multi-modal generative topic modeling approach for IPF.The previously developed method 29 is adapted to IPF.
Step 2: To identify the receptors for the ligands in 1 (via CellChatDB as described in Fig. 4).
Step 4: To identify the ligands for the receptors in 3 (via CellChatDB as described in Fig. 4).
Vol www.nature.com/scientificreports/ Step 5: To identify downstream and upstream targets of the ligand-receptor pairs found in Steps 2 and 4 by Kyoto encyclopedia genes and genomes (KEGG) pathway analysis and to select those that are among the 83 latent IPF-features identified by the multi-modal generative toping modeling.
Step 6: To identify cell-types where the ligands-receptors and their downstream and upstream singling targets found in Steps 1-5.
Our aim is to identify the inter-organ mechanism of IPF.The multi-modal generative topic-model found a possible involvement of the liver in this mechanism (Fig. 2).Hence, the ligand-receptor pair(s) that bridge the lung (the primary organ of IPF pathology) and the liver could be such a mechanism.Furthermore, to fulfill this  S3.
mechanism, the expression of the non-lung component of the ligand-receptor pair should be enriched in the liver.
On the basis of this rationale, we examined the expression patterns of the non-lung components of the ligand-receptor pairs in the liver (Fig. 5, Supplementary Table S5).The analysis of the multi-organ human singlecell RNA sequencing (scRNA-seq) database, Tabula Sapiens, identified two pairs, KNG1 (the liver)-BDKRB1 (the lung) and IL6 (the lung)-IL6R/IL6ST (the liver), that could establish the lung-liver inter-organ mechanism.The expression of KNG1, the ligand for BDKRB1, is most enriched in hepatocytes, with lesser expression in the endothelial cells, fibroblasts, intrahepatic cholangiocytes, and T cells.The expression of IL6R/IL6ST, the receptor complex for IL6, is enriched in the endothelial cells of the hepatic sinusoid, intrahepatic cholangiocytes, and hepatocytes.S1 and S4, respectively.
Table 1.The ligands encoded by the IPF-DEgenes and their receptors.The ligands are IPF-DEgenes.The evidence for each ligand-receptor pair is indicated as described in CellChatDB.ACKR1 atypical chemokine receptor 1, ACKR3 atypical chemokine receptor 3, CCL18 C-C motif chemokine ligand 18, CD44 cluster of differentiation 44,   result identified over twofold downregulation of the IL6 expression in endothelial cells and dendritic cells in the IPF lung.In addition, in the lung macrophage, its nearly twofold downregulation was also found.While the expression of BDKRB1 is detected more ubiquitously in the lung (Fig. 6A, Supplementary Table S6), it is nearly 1000-fold upregulated in the macrophages of the IPF lung, as compared to those of the healthy lung (Fig. 6B, Supplementary Table S7).
We also examined the expression patterns of their downstream and upstream signaling targets (Fig. 7).The most significant upregulation of CALM1/CALM2/CALM3, the downstream targets of the KNG1-BDKRB1 signaling and the upstream targets of the IL6-IL6R/IL6ST signaling, was detected in macrophages in the IPF lung (Fig. 7A, Supplementary Table S6).Lesser but statistically significant upregulation for one or more of these targets was also found in fibroblasts, dendritic cells, T/natural killer T (T/NKT) cells, ciliated cells, monocytes, mast cells, and alveolar type II cells (AT2) cells.Small but statistically significant downregulation was observed for CALM1 in alveolar type I cells (AT1) cells, AT2 cells, and club cells.Such downregulation was also detected for CALM3 in monocytes and dendritic cells.The expression pattern of PCK1, the downstream target of IL6-IL6R/IL6ST signaling, was examined in the liver (Fig. 7B, Supplementary Table S6).The result shows its highest expression in hepatocytes.Its less abundant expression is detected in endothelial cells, erythrocytes, intrahepatic cholangiocytes, plasma cells, and T cells.Ligands Receptors  S6. nk cell: natural killer cell.(B)

Ligands Receptors
The differential expression of IL6 and BDKRB1 in each cell-type in the IPF-lung is shown as dot.The celltypes are indicated on the left.The differential expression of IPF vs. non-IPF is indicated as log 2 fold change ("log2FoldChange").The dot size indicates the statistical significance of the differential expression as − log 10 padj ("− log10padj")-the larger size indicating more significant (i.e., less padj values).The blue and gray colors indicate padj < 0.05 and padj ≥ 0.05, respectively.The raw data are available as Supplementary Table S7.padj adjusted p-value, AT1 cells alveolar type I cells, AT2 cells alveolar type II cells.
Step 7: To construct the inter-organ map on the basis of 1-6 results.
We put together the results obtained through Steps 1-6 and generated a landscape representing an inter-organ mechanism of IPF (Fig. 8).The logic is as follows: KNG1, expressed in the hepatic cells (Fig. 5), is the systemic ligand for its receptor, BDKRB1 (Table 2).BDKRB1 is also one of the 112 IPF-DEgenes expressed in the pulmonary cells (Table 1, Fig. 6B, Supplementary Table S1).Hence, the hepatic KNG1 directly interacts with pulmonary BDKRB1 across these organs (Fig. 8).CALM1/CALM2/CALM3, the latent IPF-features (Supplementary Table S1) are the known downstream targets of KNG1 (ligand)-BDKRB1 (receptor) signaling (KEGG: hsa05200, Pathways in cancer) (Table 4).In addition, CALM1/CALM2/CALM3 are also known upstream signaling components of the IL6 signaling (KEGG: hsa05163, Human cytomegalovirus infection pathway) (Table 3).CALM1/CALM2/ CALM3 are expressed in the pulmonary cells and their expression is upregulated in the pulmonary macrophages and fibroblasts, etc. of the IPF lung (Fig. 7A).IL6 is one of the IPF-DEgenes (Fig. 6B, Supplementary Table S4) and is a systemic ligand for its receptor, IL6R/IL6ST (Table 1).IL6R/IL6ST complex is expressed in hepatic cells (Fig. 5).Hence, the signal from the liver is transduced to the lung via KNG1 (ligand)-BDKRB1 (receptor) interaction across these organs via CALM1/CALM2/CALM3 to the IL6 signal in the lung (Fig. 8).This pulmonary IL6 signal is transduced back to the liver via the IL6 (ligand)-IL6R/IL6ST (receptor) interaction in the liver (Fig. 8).PCK1, one of the IPF disease-omics features, is a known signaling molecule for the IL6 (ligand)-IL6R/ IL6ST (receptor) interaction (KEGG: hsa04151, PI3K-Akt signaling pathway), and it is expressed in the hepatic cells (Fig. 7).Hence the IL6 signal from the IPF-lung is transduced in the liver via PCK1 signaling molecule (Fig. 8).With this logic, the mechanism described in Fig. 8 is generated.In this mechanism, the liver-derived KNG1 activates the CALM1/CALM2/CALM3 signaling pathway via BDKRB1 in the lung.This signal amplifies the expression and/or secretion of IL6 from the lung.The systemic IL6 activates the PCK1 signaling pathway via  S5.nk cell natural killer cell. Vol.:(0123456789)

Discussion
While the results shown in this study are computational, there are mounting pre-clinical and clinical evidence supporting our findings.They are as follows (below).

The latent relatedness of IPF to non-pulmonary diseases
By applying the multi-modal generative topic modeling to the multi-modal disease-omics data of 6,955 human diseases, we identified molecular and genetic relatedness of IPF to non-respiratory diseases such as various types of neoplasm, autoimmune disorders, diabetes, Alzheimer's disease, rheumatoid arthritis, obesity, cardiovascular diseases (atherosclerosis, arteriosclerosis, hypertensive disease, etc.), systemic lupus erythematosus, and multiple sclerosis (Fig. 3).A possible similarity of IPF to lung cancer is discussed in an editorial article 30 .In this article, rhodopsin guanine nucleotide exchange factors (RhoGEF) mediated epithelial cell transformation (ECT) 2 of AT2 cells in the lung could be a common mechanism between IPF and the lung cancer.Our topic modeling study shows the relatedness of IPF to diverse types of cancer (Fig. 3).As AT2 is a relatively specific resident celltype in the lung, it is unlikely that the same mechanism is the basis of the relatedness of IPF to other non-lung cancers.However, a possibility of the ECT of other types of epithelial cells in non-lung tissues remains, which could explain the relatedness of IPF to other types of cancer as found in our study.The relatedness of IPF to diabetes is another finding worth discussion.There are several clinical studies including clinical meta analyses suggesting an association between IPF and diabetes [31][32][33][34] .Our computational study also indicates molecular relatedness of IPF to diabetes (Fig. 3).Moreover, other IPF-related diseases found in this study such as Alzheimer's disease, obesity, cardiovascular diseases, systemic lupus erythematosus are linked to diabetes [35][36][37][38][39] .Furthermore, the two signaling nodes in the inter-organ mechanism of IPF proposed in our study (Fig. 8), CALM1/CALM2/CALM3 (in the lung) and PCK1 (in the liver), are both molecularly linked to diabetes (Supplementary Table S1).Taken together, may share the same molecular underpinnings with diabetes and other related diseases (i.e., Alzheimer's disease, obesity, cardiovascular diseases, systemic lupus erythematosus).

The inter-organ mechanism of IPF
Our generative computational approach predicts a molecular crosstalk mechanism between the lung and the liver for IPF (Fig. 8).The possibility of the lung-liver interaction in IPF is further supported by a clinical observation of liver fibrosis in some IPF patients 40 .
In the lung-liver interaction mechanism that we found, two secreted systemic factors, KNG1 and IL6, bridge the liver-lung crosstalk.Hence, based on this mechanism, the interference of KNG1-BDKRB1(a receptor for  41 . In the same study, it is also shown that IPF patients exhibit the increased level of soluble interleukin 6 receptor subunit alpha (sIL-6Rα) in their lung tissues.However, the proposed therapeutic mechanism is the blocking of the intra-pulmonary interactions of sIL-6Rα and IL6.Furthermore, possible roles of interleukins in the pathogenesis of pulmonary fibrosis including IPF are recently discussed 42 .In these other studies, the IL6 inhibitory effects in IPF patients are discussed only in the context of heterologous cell-cell crosstalk within the lung tissue.However, as the IL6R is also present in hepatocytes, hepatic endothelial cells, and intrahepatic cholangiocytes (Fig. 5), such IL6-IL6R inhibitory effect could also occur in the liver.Hence, when the therapeutic inhibition of the IL6-IL6R interaction is effective, it is important to consider a possibility that such effects are also through the inhibition of this ligand-receptor interaction outside the lung tissue such as within the liver tissue.
In our inter-organ mechanism, we also propose that the calmodulin pathway (CALM1/CALM2/CALM3) is activated by the liver-derived KNG1 interaction with the lung BDKRB1, which then induces the IL6 pathway (Fig. 8).It is shown that a calmodulin inhibitor, trifluoperazine, exhibits an anti-inflammatory effect in a bleomycin-induced pulmonary fibrosis animal model 43 .This pre-clinical evidence supports our proposed mechanistic model.
The other signaling node in our inter-organ mechanism is PCK1 (Fig. 8).In our model, PCK1 pathway is activated by the IL6-IL6R interaction in the liver, which is feeds back to the lung pathogenesis of IPF via KNG1-BDKRB1 pathway.Recently, nintedanib, one of the two FDA-approved IPF therapeutics, is shown to attenuate experimental colitis via inhibiting the PCK1 pathway 44 .This study suggests that a part of the therapeutic effect of nintedanib on IPF is via the inhibition of the PCK1 pathway.
There are two pending questions in the proposed inter-organ mechanism of IPF.The ligands, receptors, and their signaling targets in this model are co-expressed in multiple cell types in their corresponding organs (Fig. 8).Hence, it remains unknown whether the KNG1-BDKRB1 and IL6-IL6R/IL6ST pathways function within the same cell-type or they interact in trans across different cell-types within the same organ.
Another question is whether the IL6-IL6R/IL6ST signal feeds back to KNG1 via PCK1 (Fig. 8).While the signaling of IL6-IL6R/IL6ST to PCK1 is established (hsa04151 KEGG pathway, PI3K-Akt signaling pathway in human), the link of PCK1 to KNG1 remains unknown.Upon the experimental validation of this link, the model becomes a closed feedforward and feedback "loop" across the liver and the lung.
These questions remain for the future studies and their results provide more detailed mechanistic description of IPF.Furthermore, they facilitate the designing of first-in-class therapeutic and/or diagnostic strategies for IPF.
In this study, we exploited a growing body of multi-modal disease-omics data and a generative computational power to predict an inter-organ mechanism of IPF with the molecular and cellular resolution.Furthermore, our retrospective reference-mining found multiple experimental and clinical evidence in support of the predicted mechanism as described above.Our proposed mechanism is detailed enough, providing a unique opportunity to design hypothesis-driven pre-clinical experiments and/or clinical studies to discover and evaluate first-in-class therapeutic and diagnostic targets for IPF.In addition, our study and results illustrate a computational framework to generate experimentally-testable mechanistic models for other diseases where very little mechanism is known.

Multi-modal generative topic modeling
The multi-modal generative topic modeling approach is as previously described 29 .This topic modeling approach exploits the similarities among diseases on the basis of their multi-modal omics features.In this study, we deleted IPF disease-omics data to identify latent IPF-features (see also Fig. 1 and the details in "Result" section).

Organ-and cell-type expression patterns of the latent IPF-features
The organs and cells where the IPF-features are expressed were identified by organ/cell enrichment analyses using THE HUMAN PROTEIN ATLAS v 21.1 [23][24][25] , as previously described 29 .Briefly, we generated a 2 × 2 contingency table showing the number of the genes of interest that are associated with the target organ(s)/cell(s), and performed chi-square test of independence by using the contingency table.

Latent relatedness of IPF to other diseases
We identified diseases to which IPF is related as previously described 29 .Briefly, the disease-labels of each latent IPF-feature were identified in the Dataset A (Fig. 1) and the frequency of each disease-label was counted.The disease-labels of the higher-frequency are determined as more related to IPF.

Lung RNA-seq data from patients
The studies with human subjects and data were approved by the Institutional Review Board of Advanced Telecommunications Research Institute International on behalf of Karydo TherapeutiX, Inc. (Approved Number: HK2101-2101, HK2101-2103, HK2101-2202) and of National Institutes of Biomedical Innovation, Health and Nutrition (Approved Number: 187) and of Kanagawa Cardiovascular and Respiratory Center (Approved Number: KCRC-19-0015).The informed consent was obtained from all subjects.All methods were performed in accordance with the relevant guidelines and regulations.The lung tissues were collected from 299 subjects.They consist of 173 idiopathic interstitial pneumonias (IIPs), 76 hypersensitivity pneumonitis (HP), 26 connective tissue diseases (CTD), 24 others (other interstitial lung diseases).The 173 IIPs are further composed of 95 IPF, 41 unclassifiable interstitial pneumonia (UCIP), 28 idiopathic nonspecific interstitial pneumonia (NSIP), 3 idiopathic pleuroparenchymal fibroelastosis (PPFE), and 6 other IIPs.RNA was purified from each sample and processed for RNA sequencing as follows: The lung tissues were sent to TAKARA BIO INC. (Shiga, Japan) for Figure 1.General overview of the multi-modal generative topic modeling approach for IPF.The previously developed method29 is adapted to IPF.

Figure 2 .
Figure 2. The organ and cell-enrichment analyses of the latent IPF-features.(A) The organ enrichment.(B)The cell-type enrichment.The enrichment level of the 83 IPF-features in each organ and each cell-type is shown as bar-graph of −log10(q-values) in the descending order.The q-value (qvalue) = 0.05 (the threshold for the statistical significance) is indicated as a red line in each graph.The raw data are available as Supplementary TableS2.

Figure 3 .
Figure 3.The latent diseases to which IPF is molecularly related.The frequency of the appearance of the 83 IPF-features in each disease is indicated as "count".Shown are the diseases of which counts are above 20 in the descending order.The long disease names are cut short and indicated as "... " at their ends.The raw data are available as Supplementary TableS3.

Figure 4 .
Figure 4. General overview of the computational framework to generate an inter-organ mechanism of IPF.See the "Methods" section for the detailed step-by-step description.The 83 latent IPF-features and 112 lung DEgenes (IPF vs. non-IPF) are found in Supplementary TablesS1 and S4, respectively.

Figure 5 .
Figure 5.The hepatic expression of the ligands and receptors for the IPF pulmonary receptors and ligands.The level of each ligand and receptor in each cell-type in the liver is shown as dot.The size and the heat-intensity represent the ratio of cells expressing the gene in each cell-type cluster and the mean expression level of logtransformed counts [i.e., log(1 + count per 10,000)], respectively, as shown on the right side of the panel.The raw data are available as Supplementary TableS5.nk cell: natural killer cell.

Figure 6 .
Figure 6.The expression of IL6 and BDKRB1 in the lung.(A) The level of each ligand and receptor (including IL6 and BDKRB1) in each cell-type in the lung of the healthy subjects (Tabula Sapiens) is shown as dot.The size and the heat-intensity represent the ratio of cells expressing the gene in each cell-type cluster and the mean expression level of log-transformed counts [i.e., log(1 + count per 10,000)], respectively, as shown on the right side of the panel.The raw data are available as Supplementary TableS6.nk cell: natural killer cell.(B) The differential expression of IL6 and BDKRB1 in each cell-type in the IPF-lung is shown as dot.The celltypes are indicated on the left.The differential expression of IPF vs. non-IPF is indicated as log 2 fold change ("log2FoldChange").The dot size indicates the statistical significance of the differential expression as − log 10 padj ("− log10padj")-the larger size indicating more significant (i.e., less padj values).The blue and gray colors indicate padj < 0.05 and padj ≥ 0.05, respectively.The raw data are available as Supplementary TableS7.padj adjusted p-value, AT1 cells alveolar type I cells, AT2 cells alveolar type II cells.

Figure 7 .
Figure 7.The expression of the signaling targets in the liver and the lung.(A) The differential expression of CALM1/CALM2/CALM3 in each cell-type in the IPF-lung is shown as dot.The cell-types are indicated on the left.The differential expression of IPF vs. non-IPF is indicated as log 2 fold change ("log2FoldChange").The dot size indicates the statistical significance of the differential expression as − log 10 p-adj ("− log10padj")-the larger size indicating more significant (i.e., less padj values).The blue and gray colors indicate padj < 0.05 and padj ≥ 0.05, respectively.The raw data are available as Supplementary TableS7.padj: adjusted p-value; AT1 cells: alveolar type I cells; AT2 cells: alveolar type II cells.T/NKT cells: T/natural killer T cells.(B) The level of PCK1 in each cell-type in the liver is shown as dot.The size and the heat-intensity represent the ratio of cells expressing the gene in each cell-type cluster and the mean expression level of log-transformed counts [i.e., log(1 + count per 10,000)], respectively, as shown on the right side of the panel.The raw data are available as Supplementary TableS5.nk cell natural killer cell.